Chemical processing plants are designed to carry out various complex, often non-linear, chemical processes, such as a polymer process. The processing plant design typically involves a series of various pieces of equipment, e.g., stirring tanks, evaporators, reactors, pumps, feed conduits and the like, and a control system for monitoring and maintaining settings of the various pieces of equipment to effect needed operating conditions (e.g., volume, flow rate, stirring rate, temperature, pressure, feed composition, etc.). Typically at the output end, the resulting product is analyzed in a laboratory for quality outcome. Product quality is commonly measured in terms of physical properties (or polymer properties), e.g., melt index, density, etc. Thus it is desired that these properties achieve certain values or value ranges, such that the product is termed “on spec”.
In order to effect a change in physical properties and hence product quality, equipment settings, input and hence operating conditions are changed. To the extent that these changes are upstream in the chemical/polymer process, there is a time lag before a change in product quality/physical properties occurs and the full impact of the upstream changes is in effect. There is a further time lag from the time a product sample is taken at the output end of the process to the time the lab analysis with that sample is made. Thus it is desirable to have a means for predicting the downstream effect in product quality/physical properties for a given upstream change in operating condition. In the polymer processing industry, so-called “inferential sensors” or “soft sensors” are such predictive means.
By way of background, polymer processes are known to be highly non-linear. Polymer properties are complex, non-linear functions of polymer process plant operating conditions (process variables). Currently there are two methods of developing estimates of polymer properties such as melt index (MI) and density typically used for describing polymer product quality. The first method is a regression based inferential that uses neural networks, partial least squares (PLS), and other conventional regression methods. The second method employs State Estimation Models (SEM) which use an auto-calibrated (self-adjusting), non-linear, online rigorous dynamic model. Specifically, State Estimation Models provide a mass and energy balance model of the entire plant and thus involves equations for heat and mass balances, polymer thermodynamics and kinetics; Process geometries and control strategies.
The first method being a regression is only valid in the area where polymer property data has been collected in related areas of operation. Its advantage is that it is easy to use by plant engineers and is relatively inexpensive. One example implemented as a software tool is Aspen IQ™ by Aspen Technology, Inc. of Cambridge, Mass.
The second method is rigorous which means that it can extend its predictions beyond the range of data available. The advantage provided is that when new polymer products are made, for which there is no data, the probability of the model predicting correctly is higher for the rigorous method than for the regressed method. The disadvantage with the rigorous method is that it is difficult to implement and maintain with all its equations related to heat and mass balances, polymer thermodynamics, kinetics, process geometries and control strategies.